No abstract

Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. Crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct of crows. In this paper, a hybrid GWO with CSA, namely GWOCSA is proposed, which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid GWOCSA, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities is used in this paper. The results obtained by the proposed algorithm are compared to 10 other algorithms in this paper for verification. The statistical results demonstrate that the GWOCSA outperforms other algorithms, including the recent variants of GWO called, enhanced grey wolf optimizer (EGWO) and augmented grey wolf optimizer (AGWO) in terms of high local optima avoidance ability and fast convergence speed. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems, the GWOCSA is also employed to solve the feature selection problem as well. The GWOCSA as a feature selection approach is tested on 21 widely employed data sets acquired from the University of California at Irvine repository. The experimental results are compared to the stateof-the-art feature selection techniques, including the native GWO, the EGWO, and the AGWO. The results reveal that the GWOCSA has comprehensive superiority in solving the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. INDEX TERMS Grey wolf optimizer, crow search algorithm, hybrid algorithm, function optimization, feature selection.

No abstract

No abstract

The utilisation of renewable energy sources (RES) in increasing drastically because of various issues including depletion of fossil fuels, greenhouse gas emissions, climate change and so on. As the power generated from RES is fluctuating in nature, therefore, the appropriate sizing of the hybrid model based on RES is utmost important. In this study, the grey wolf optimisation, a newly developed approach is used for the optimal sizing of the hybrid model. In this work, the optimal design of solar/biomass/biogas/battery‐based hybrid system has been carried out to supply continuous electricity to various households of a cluster of villages of Haryana state of India. The results obtained from the proposed model have been compared with harmony search and particle swarm optimisation and found better.